Kalshi F1 Points Model Trader
/install kalshi-f1-points-model-trader
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Kalshi F1 Points Model Trader\r
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This is a template. \r The default signal uses static points standings and driver ratings to Monte Carlo simulate the remaining season -- remix it with live F1 API data, qualifying pace analysis, or weather-adjusted race models. \r The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.\r \r
Strategy Overview\r
\r F1 Drivers Championship markets on Kalshi price each driver's chance of winning the title. This skill runs Monte Carlo simulations using current points standings and driver skill ratings to compute fair win probabilities, then trades when the market diverges from the model.\r \r Key advantages:\r
- Points standings are public -- no proprietary data needed\r
- Monte Carlo captures non-linear dynamics -- points gaps, remaining races, and driver variance\r
- Driver ratings provide edge -- market often misprices mid-field drivers\r \r
Signal Logic\r
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Points-Based Monte Carlo Model\r
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- Load current F1 Drivers Championship standings\r
- Assign driver skill ratings (affects finishing position distribution)\r
- Simulate remaining races 10,000 times with weighted random finishes\r
- Count championship wins per driver to get win probabilities\r
- Compare model probability to Kalshi market price\r
- Trade when
|model - market| >= entry_edge\r \r
Conviction-Based Sizing\r
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conviction = min(|edge| / entry_edge, 2.0) / 2.0\rsize = max($1.00, conviction * MAX_POSITION_USD)\r- Larger edge = larger position, capped at MAX_POSITION_USD\r \r
Remix Ideas\r
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- Live F1 API: Replace static standings with real-time Ergast/OpenF1 API\r
- Qualifying pace model: Weight recent qualifying gaps for more accurate finishing distributions\r
- Constructor performance: Factor in car development trajectory\r
- Weather/track type: Adjust driver ratings for rain races or street circuits\r \r
Risk Parameters\r
\r | Parameter | Default | Notes |\r |-----------|---------|-------|\r | Entry edge | 10% | Min model-vs-market divergence to trade |\r | Exit threshold | 45% | Sell when position price reaches this |\r | Max position size | $5.00 USDC | Per market |\r | Max trades per run | 5 | Rate limiting |\r | Max slippage | 15% | Skip if slippage exceeds |\r | Min liquidity | $0 | Disabled by default |\r \r
Installation & Setup\r
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clawhub install kalshi-f1-points-model-trader\r
```\r
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Requires: `SIMMER_API_KEY` and `SOLANA_PRIVATE_KEY` environment variables.\r
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## Cron Schedule\r
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Cron is set to `null` -- the skill does not run on a schedule until you configure it in the Simmer UI.\r
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## Safety & Execution Mode\r
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**The skill defaults to dry-run mode. Real trades only execute when `--live` is passed explicitly.**\r
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| Scenario | Mode | Financial risk |\r
|----------|------|----------------|\r
| `python trader.py` | Dry run | None |\r
| Cron / automaton | Dry run | None |\r
| `python trader.py --live` | Live (Kalshi via DFlow) | Real USDC |\r
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The automaton cron is set to `null` -- it does not run on a schedule until you configure it in the Simmer UI. `autostart: false` means it won't start automatically on install.\r
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## Required Credentials\r
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| Variable | Required | Notes |\r
|----------|----------|-------|\r
| `SIMMER_API_KEY` | Yes | Trading authority. Treat as a high-value credential. |\r
| `SOLANA_PRIVATE_KEY` | Yes | Base58-encoded Solana private key for live trading. |\r
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## Tunables (Risk Parameters)\r
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All risk parameters are declared in `clawhub.json` as `tunables` and adjustable from the Simmer UI without code changes.\r
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| Variable | Default | Purpose |\r
|----------|---------|---------|\r
| `SIMMER_F1_PTS_ENTRY_EDGE` | `0.10` | Min divergence between model and market to trigger trade |\r
| `SIMMER_F1_PTS_EXIT_THRESHOLD` | `0.45` | Sell position when price reaches this level |\r
| `SIMMER_F1_PTS_MAX_POSITION_USD` | `5.00` | Max USDC per trade |\r
| `SIMMER_F1_PTS_MAX_TRADES_PER_RUN` | `5` | Max trades per execution cycle |\r
| `SIMMER_F1_PTS_SLIPPAGE_MAX` | `0.15` | Max slippage before skipping (0.15 = 15%) |\r
| `SIMMER_F1_PTS_MIN_LIQUIDITY` | `0` | Min market liquidity USD (0 = disabled) |\r
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## Dependency\r
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`simmer-sdk` is published on PyPI by Simmer Markets.\r
- PyPI: https://pypi.org/project/simmer-sdk/\r
- GitHub: https://github.com/SpartanLabsXyz/simmer-sdk\r
- Publisher: [email protected]\r
\r
Review the source before providing live credentials if you require full auditability.\r
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install kalshi-f1-points-model-trader - 安装完成后,直接呼叫该 Skill 的名称或使用
/kalshi-f1-points-model-trader触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Kalshi F1 Points Model Trader 是什么?
Trades F1 Drivers Championship winner markets on Kalshi using current points standings and Monte Carlo simulation to compute win probabilities. Requires SIMM... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 93 次。
如何安装 Kalshi F1 Points Model Trader?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install kalshi-f1-points-model-trader」即可一键安装,无需额外配置。
Kalshi F1 Points Model Trader 是免费的吗?
是的,Kalshi F1 Points Model Trader 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Kalshi F1 Points Model Trader 支持哪些平台?
Kalshi F1 Points Model Trader 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Kalshi F1 Points Model Trader?
由 diagnostikon(@diagnostikon)开发并维护,当前版本 v1.0.0。